Systems and methods for performing image analysis

ABSTRACT

Methods and systems for automating the management and processing of roof damage analysis. In some embodiments image data associated with damaged roofs is collected and automatically analyzed by a computing device. In some embodiments, the image data is modified automatically to include descriptive metadata and visual indicia marking potential areas of damage. In one embodiment, the systems and methods include a remote computing device receiving visual data associated with one or more roofs. In one embodiment, insurance company specific weightings are determined and applied to received information to determine a type and extent of damage to the associated roof. In one embodiment, results of the methods and systems may be used to automatically generate a settlement estimate or supplement additional information in the estimate generation process.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part and claims the benefit ofU.S. application Ser. No. 15/445,509, filed Feb. 28, 2017, avowed andnow U.S. Pat. No. 10,181,079 which claims the benefit of U.S.Provisional Application No. 62/301,411, filed Feb. 29, 2016, the entiredisclosure of each of which is hereby incorporated herein by reference.

TECHNICAL FIELD

The present disclosure is directed generally toward property damagerecognition systems and methods and more particularly toward systems andmethods for utilizing video and/or still images to analyze types ofdamage to property.

BACKGROUND

Homes and commercial buildings may experience damage or otherwise benegatively impacted due to fires, earthquakes, tornados, flooding, andother disasters. Such disasters may be of natural causes, or they mayresult from mechanical failure, human error, or any number of othernon-natural causes. As an example, flooding may result from a widevariety of natural conditions, including excessive rain, storm surges,or rapid melting of snow or ice. Additionally, freezing temperatures maycause the water inside water pipes to freeze, expand and burst thepipes. Water hoses may be become disconnected or may become brittle andbreak. Sinks and commodes may overflow from clogged pipes. As anotherexample, fire can result from natural causes, such as lightning strikes,or it can result from human-related causes, such as a gas leak resultingin gas buildup, ignition and “puff back”; a stove or oven that becomesexcessively hot; an overloaded electrical circuit; or a curling ironleft in close proximity to a flammable material. The cause of damage toproperty may come from any number of sources and the damage caused tothe property typically varies greatly with each and every cause in anynumber of ways related to the scope and magnitude of the damage.

The damage caused by water, fire, hail or other disasters is rarely easyto identify, or even limited to the area where the mishap occurred. Forexample, hail may damage a roof in places which are difficult to viewand/or access. As a second example, a pipe may suffer a break that isconfined to a particular location, but broken pipes often lead toflooding, which may be widespread throughout an entire structure and thescope of such flooding may be impossible to determine during simpleinspection. Likewise, even though a fire may be contained to aparticular room or location in a building, it may cause smoke damagethroughout the entire building or even adjacent buildings in places noteasily accessible. Moreover, the building may suffer water damage and/orother types of damage as a result of efforts to extinguish the fire.Such damage may affect the structure of a property in ways that areimpossible to determine without extensive testing or, in some cases,actual demolition of the property.

When a damaged structure is insured, the first step in disastermitigation and restoration often involves notifying the insurancecompany of the damage or loss. The insurance company then typicallydispatches a person, e.g. a vendor or adjuster, to physically andpersonally visit the damaged location to assess the loss and write aninitial mitigation estimate that addresses the initial loss and anysecondary damages. Alternatively, the insured party may call a vendordirectly, personally provide a description of the damage to receive aninitial mitigation estimate from the vendor, and then contact theinsurance company.

Methods of inspecting property damage typically also require physicalpresence of an analyst on or near the damaged building. Such methodslimit the analysis to buildings which the analyst or inspection companyhas obtained permission to inspect. In many cases, however, damagecaused by nature, e.g. hail or high-winds, is widespread andfar-reaching across city blocks, neighborhoods, towns and counties. Foran insurance or inspection company to obtain permission to physicallyaccess each and every damaged property after a storm may require anexhausting, time-consuming and expensive process.

Also, an owner of a damaged property or an insurance company may needestimates from a number of repair companies in order to generate anestimate of damage to the property. Each repair company, in order tosupply its own estimate or bid for cost of repairing the property mayneed its own analyst to physically visit the property. As such, oneestimate to one damaged property may require a multitude of analystsfrom different repair companies each visiting and analyzing theproperty. In the case of widespread damage across even one neighborhoodafter a storm a proper analysis of the damage would require anenormously time-consuming and expensive process before an estimate canbe generated. These expenses and time delays add up and add a great dealof economic waste with each individual property damaged.

Accuracy of damage estimates relies on accurate analysis. Analysisaccuracy and techniques may also vary between analysts making itdifficult and/or impossible to set industry-wide standards.

In today's world, due to a human not being efficient at looking at theentire roof for hail damage, most insurance adjusters take arepresentative sample to determine if there is hail damage for adirectional slope on the roof. This is not always representative of theentire condition of a directional slope on the roof.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present disclosure and itsadvantages, reference is now made to the following description taken inconjunction with the accompanying drawings, in which like referencenumerals represent like parts:

FIG. 1 illustrates a hardware block diagram of an exemplary system forperforming video or still image analysis on building structures inaccordance with embodiments described herein;

FIG. 2 is a flowchart illustrating an exemplary method of generating afeature list in accordance with embodiments described herein;

FIG. 3 is a flowchart illustrating an exemplary method of generate adescription of features in accordance with embodiments described herein;

FIG. 4 is a flowchart illustrating an exemplary method of applyingweighting and determining types and extents of damage to roofs inaccordance with embodiments described herein; and

FIG. 5 is a flowchart illustrating an exemplary method of automaticallyidentifying covered property damage in accordance with embodimentsdescribed herein.

SUMMARY

It is with respect to the issues and other problems presently faced bythose of skill in the relevant art and described above that theembodiments presented herein are contemplated. What is needed is aproperty damage analysis system which (1) is automated or otherwise doesnot require a human analyst to scale a damaged building, (2) isefficient and not time-consuming, (3) is reliable and provides accurate,standardized analysis, and (4) is capable of analyzing damage indifficult to access areas.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which this disclosure belongs. It willbe further understood that terms, such as those defined in commonly useddictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andthis disclosure.

As used herein, the singular forms “a,” “an,” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof.

The phrases “at least one,” “one or more,” and “and/or” are open-endedexpressions that are both conjunctive and disjunctive in operation. Forexample, each of the expressions “at least one of A, B and C,” “at leastone of A, B, or C,” “one or more of A, B, and C,” “one or more of A, B,or C,” and “A, B, and/or C” means A alone, B alone, C alone, A and Btogether, A and C together, B and C together, or A, B and C together.

The term “automatic” and variations thereof, as used herein, refers toany process or operation done without material human input when theprocess or operation is performed. However, a process or operation canbe automatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

Hereinafter, “in communication” shall mean any electrical connection,whether wireless or wired, that allows two or more systems, components,modules, devices, etc. to exchange data, signals, or other informationusing any protocol or format.

The terms “determine,” “calculate,” and “compute,” and variationsthereof, as used herein, are used interchangeably and include any typeof methodology, process, mathematical operation or technique.

Various embodiments of the present disclosure describe a system with theability to employ a machine to analyze damage to property in a number ofways. For example, embodiments of the present disclosure present asystem capable of identifying probable hail damage. Embodiments mayinclude systems capable of identifying locations of identified probablehail damage. Embodiments may also include systems capable of identifyingwhether or not an entire roof has been subjected to hail damage.Embodiments may include systems which determine such informationregarding roof damage through the analysis of video and/or still-imagesof the roof. It should be appreciated that as processing speeds andtechnology for video and images are increased and automated, anautomated solution could be highly cost effective and much safer thanhaving a human climb a roof to personally inspect the roof.

Embodiments presented herein further disclose a system comprising: animage receiver that receives one or more original roof images; a featureextractor that is used to identify features and corresponding locationsof the identified features from the one or more original roof images ora processed version thereof; a feature analyzer that generates a featurelist describing the identified features and the corresponding locationsof the identified features in a format that is deliverable to anautomated settlement engine. According to this embodiment, the automatedsettlement engine comprises: an image analysis system ApplicationProgramming Interface (API) configured to receive the feature list fromthe image analysis system; and, a set of image analysis rules that areconfigured to analyze the identified features from the feature listalong with the corresponding locations of the identified features todetermine whether or not damage occurred to a property with respect to apredetermined likelihood.

Embodiments presented herein further disclose a system comprising: animage receiver tool that receives one or more original images; anextractor tool that identifies one or more features and theircorresponding locations; an analyzer tool that processes the informationobtained by the extractor tool and delivers the one or more identifiedfeatures and their corresponding locations to an automated settlementengine; an annotation tool that allows one or more features to be markedor further described by a user; wherein the automated settlement engineapplies one or more rules configured to determine whether sufficientdamage occurred; wherein when the application of the one or more rulesdictate that damage occurred with a probability greater than thepredetermined likelihood, then the system generates a report thatindicates damage has occurred and identifies the corresponding locationssubject to insurance coverage and reimbursement; and wherein when theapplication of the one or more rules dictate that damage occurred with aprobability less than the predetermined likelihood, then the systemgenerates a report that indicates a manual inspection of the property isneeded.

Embodiments of the present disclosure are further capable of consideringor suggesting a manual inspection for information from additional out ofthe line of site characteristics (e.g., the underside of the shingle matis fractured, and considering damages to soft metals and plastics).Considerations of this additional information not obtained from videoand/or still images can be used to weight a probability score as towhether or not the roof actually experienced hail damage in a particularlocation or along its entirety. Embodiments of the present disclosurecould also weigh these factors (e.g., video/still image informationalong with additional information not obtained from video and/or stillimages).

In some embodiments, each carrier represented in a settlement guidelineengine could set their probability algorithm (scores) based on theseattributes, meaning that each insurance carrier could define differentweighting algorithms and/or identify which features should be consideredor not considered by the automated process that analyzes roof damage forspecific markers of hail damage. The system could be configured toconsume photos or video to make these assessments.

Other system features include bookmarking components of the video,tagging potential marks, ongoing learning based on human review of thesemarks, insurance carrier specific learning for what their teams acceptas hail that are unique overtime building out acceptable images. Thesystem can also create a digital test square which is the methodcarriers use today which can be superimposed or “slid” over the roofslope to analyze damage, such as hail count changes, within the testsquare area, which may vary based on the location. The manufacturer datafor the specific shingle may be derived and taken into account to assistthe machine better predict the likelihood that an anomaly is or is not ahail strike. Methods on analyzing layers and patterns within knownmanufacturer shingle granular spray patterns, grains per inch based onslope exposure (north versus south/tree coverage/etc.) as well as age,oxidation (age of bruise), filtering images for organic matter (lichenpatterns), and other characteristics would be leveraged to help setprobabilities of hail damage versus non-hail damage. In particular,additional layers of shingles drastically increase the probability ofdamage due to the lack of a solid surface under the shingles.Furthermore, roof pitch is also a factor due to the angle of hail impactand may affect the shape of the hail damage pattern.

Further embodiments may also consider weather reports and stormdirectionality as both weather reports and directionality of the stormprovide good indications of what will be seen.

Incorporated by reference in their entireties are the following U.S.patents and patent applications directed generally to the relevant art,and identified for the purpose of supplementing the written descriptionfor various aspects of the present disclosure. The U.S. patents andpending applications incorporated by reference are as follows: U.S. Pat.Nos. 8,983,806, 9,158,869 and U.S. Pat. Pub. Nos. 2016/0098802.

The preceding is a simplified summary to provide an understanding ofsome aspects of the embodiments. This summary is neither an extensivenor exhaustive overview of the various embodiments. It is intendedneither to identify key or critical elements nor to delineate the scopeof the embodiments but to present selected concepts in a simplified formas an introduction to the more detailed description presented below. Aswill be appreciated, other embodiments are possible utilizing, alone orin combination, one or more of the features set forth above or describedin detail below.

DESCRIPTION OF EMBODIMENTS

The ensuing description provides embodiments only and is not intended tolimit the scope, applicability, or configuration of the claims. Rather,the ensuing description will provide those skilled in the art with anenabling description for implementing the described embodiments. Itbeing understood that various changes may be made in the function andarrangement of elements without departing from the spirit and scope ofthe appended claims.

Embodiments of the present disclosure may be better understood withreference to FIGS. 1-4. Referring first to FIG. 1, an illustrativecommunication system 100 will be described in accordance with at leastsome embodiments of the present disclosure. The communication system 100of FIG. 1 may be a distributed system and, in some embodiments,comprises a communication network 104 connecting one or more imagecapture devices 108 (e.g., cameras, automated cameras/drones,smartphones equipped with cameras, video cameras, etc.) to an imageanalysis system 120 and automated settlement engine 116. While thediscussion herein is primarily directed to surveying damage to roofs, itshould be appreciated that damage to any property may be surveyed insimilar ways. Nothing in this disclosure is intended as being limited tosurveying solely roof damage.

The communication system 100 may in some embodiments include one or moreproperty systems 112. Each property system 112 may comprise a piece ofreal property such as a house, garage, or other building, or maycomprise one or more pieces of fixtures associated with a piece of realproperty, such as a roof, ceiling, wall, floor, air conditioner,furnace, etc. In some embodiments, a property system 112 may compriseone or more pieces of physical property such as an entertainment system,furniture, etc. In some embodiments, a property system 112 may comprisea combination of real property, fixtures, and physical property. Aproperty system 112 may be associated with one or more insurancepolicies.

A property system 112 may be equipped with one or more sensors. Sensorsmay be capable of communicating with network devices via a communicationnetwork 104. By utilizing sensors within a property system 112, one ormore methods and systems of automatically analyzing images may beachieved. For example, an artificial intelligence (“AI”) system may becapable of using data from a sensor or any type of computing device torecognize evidence of damage to a property. Sensors may be, for example,pressure and/or noise sensors on or near a roof of a property system112. The sensors may send data to a server executing an AI system. Usingdata from the sensors, the AI system may be capable of recognizingevidence of hail. Throughout the life of the AI system, the AI systemmay use hail-related data to update its hail hit recognitioncapabilities. In this way, the AI system may be capable of determining aprobability of whether hail damage has occurred.

Such a system may improve the efficiency of an automated insurancesystem in the aftermath of a widespread disaster such as a tornado,hurricane, or hailstorm. For example, if the AI system shows a highdegree of confidence that the data received from the sensors is a resultof hail, then the insurance company may be able to accept a highersettlement amount without being required to physically review theproperty damage. This automation may greatly reduce the amount of timeneeded to respond to thousands and thousands of claims following anatural disaster, allowing homeowners to carry on with their lives.

An AI system may use data from sensors associated with a property system112 to be capable of identifying objects requiring replacement and/orrepair. For example, objects such as: a fence, gutters, shingles, airconditioning unit, etc. Data received from sensors associated with suchobjects may be used by an AI system to enable the AI system to recognizefeatures indicating an event has occurred. An AI system may then becapable of identifying whether an event has occurred, what the damageis, and whether anything should be replaced or repaired.

For example, sensors may comprise impact sensors on top of a roof, soundsensors inside an attic (looking for a sound signature of hail sound),sound sensors elsewhere, impact sensors, image sensors, stereoscopicviews (with multiple images from different angles), different frequencyimage sensors (e.g., IR sensors, etc.), etc. Any such sensors wouldbuild outputs from a property system 112 that may be used as an input toan AI system.

An AI system may in some embodiments use simple images of non-roofassets and elevations of the house/asset as an initial input to the AIsystem. Such images may be taken by a homeowner or other person using auser device 160. Images may be associated with metadata related todetails regarding each image, such as which direction each image isfacing. If simple images indicate there may be or is likely damage to apart of a property system, such as a roof, then the AI system may decidecloser inspection may be required and an adjuster may be sent to go upon the roof or elsewhere to survey the damage.

Such information may also or alternatively be fed into an auditing tooland then an output of the auditing tool may be what is used to retrain adamage identification model used by an AI system.

An AI system may be used to survey damage other than roof damage, forexample, sensor data indicating inside damage of a flood line couldenable the AI system to automatically identify fixtures, real, orphysical property inside a house which may need to be replaced,repaired, and/or modified. Similarly, data indicating a fire may enablethe AI system to automatically identify fixtures, real, or physicalproperty inside a house which may need to be replaced, repaired, and/ormodified. Such data may comprise sensor data, photographic datacollected by a user device 160, textual data entered into a user device160 by a user, or other data.

If any damage is identified by the AI system using such data, the AIsystem may prompt a user via a user device 160 for additionalinformation on such damage assets, thus enabling a guided inspection.The AI system may automate a guided inspection and use data entered intoa user device 160 by a user during a guided inspection to build aprofile of information. If additional information is needed, then the AIsystem may send profile information to a virtual agent that may assist ahomeowner with steps to complete the damage profile information.

In some cases, multiple sets of inspection data may be received by an AIsystem for a single property system 112. The AI system may keep track ofwhat property is damaged in which inspection data and compare images anddata sets to determine whether a piece of property has already beencovered but not replaced/fixed and then make sure that damage is notcovered again unnecessarily. Because some homeowners will submit a claimbut not make the fix, the AI system may enable an insurance carrier toavoid paying for the same damage twice.

In some embodiments an AI system may be capable of identifyingsubrogation opportunities inside and outside of a house or otherproperty system 112. For example, inside a house there may be sensors orother data relating to determining when a certain piece has broken(e.g., compression fitting, washing machine hose). If something lookslike a subrogation opportunity, the AI system may prompt a user such asan adjuster to look for additional information or to ask about laborersor help identify other damage.

The AI system may be capable of automatically looking through countyrecords or other sources of information to collect data related topossible property damage. For example, the AI system may be capable ofresearching and identifying issues of quality of parts or ofinstallation and be capable of identifying any difference between thetwo. The AI system may further be capable of identifying a cause oforigin of damage (e.g., identifying a cause of loss), as well as capableof identifying potential fraud cases. The AI system may be trained toidentify whether a potential fraud case is at issue.

If identified, the AI system may be capable of simply prompting users(e.g., an everyday adjuster or homeowner rather than a specialist) withfurther questions to identify if an insurance claim looks like a fraudcase. The AI system may compare a customer's indication of asset qualityversus a guessed indication of asset quality. If they do not match orare too far apart, then the AI system could set a flag or recommend ahuman adjustor to go onsite.

In some embodiments, the AI system may be capable of identifying whatside of a house damage occurred. For example, the AI system may havedifferent models for different sides of the house (e.g., because thesouth side gets more sun and may break down easily). The AI system mayalso be capable of identifying how old some damage is, an age and/orcondition of an object to be repaired/replaced, items which are notdamaged, etc. and may be capable of building a catalog of damaged vsundamaged items.

In some embodiments, an AI system may build a damageability index andfurther use that in connection with determining that something is NOTdamaged. The AI system may be capable of identifying materials, stormdata, asset data, damage data, sensor data, etc. An AI system may alsobe capable of determining the quality of materials in an automatedfashion (look for subrogation or fraud, do not just look for quality)?

In some embodiments, the system may include one or more data capturedevices 108. A data capture device 108 may be in some embodiments ahandheld still image or video camera. In some embodiments, the datacapture device may be a smartphone or tablet or some other personalcomputing device with a camera. In some embodiments, the data capturedevice may be controlled and operated by a human operator eitherdirectly and/or physically by a handheld device 108 or remotely throughthe use of a drone or kite or some other flying device operable tocapture images or video. In some embodiments, the data may be collectedautomatically by a drone flying in an automatic pattern while recordingor taking photographs automatically.

Data collected by the data collector(s) 108 may be in the form of stillphotographs or video. Images or video may be collected by a handheldcamera or by a drone carrying a camera or by a camera-equipped kite orother type of aerial photography mechanism. The images may be takendirectly overhead or from an angle. The photos or videos may be taken bya camera capable of capturing hyper-spectral images, infrared or thermalimages and/or a common camera forming an image using visible light.

In some embodiments, one or more sensors may be used to collect dataassociated with a property. For example, impact sensors may be placed ona roof, sound sensors may be placed in an attic or another area capableof detecting impact on a roof, impact sensors, stereoscopic viewingsensors, frequency image sensors (e.g., IR sensors, etc.), etc. Soundsensors may be capable of detecting sound signatures of hail hitting theroof.

Data collected may be stored on a local memory device, e.g. onboard thecamera, or transmitted to a secondary local device, e.g. a laptop. Forexample, a data collection device 108 may be equipped with onboardmemory or a communication device operable to transmit the data tooff-board storage. Data may be tagged upon capture or at a later pointwith identifying information stored as metadata or at a later point withidentifying information stored as metadata or a folder name structure.Identifying information may include GPS data, a timestamp, insuranceplan data or other relevant information.

Data may also be live-streamed to a collecting server via a network 104.The collecting server may automatically store the received data so thatit may be easily retrieved. The data may be tagged by the collectingserver with identifying information or may be tagged by the collectingdevice prior to transmission. Data collected or stored locally may beuploaded in bulk or in portions to a server via the network 104. Thedata may be uploaded via the network 104 automatically or upon commandfrom a user operator.

Data collected by the one or more data collection devices 108 may besent to a server via the network 104. The data may be sent ortransmitted via a wireless network e.g. an LTE or other high-speedwireless communication network or via a wireless internet, e.g. WIFI,connection, or by wired connection, e.g. Ethernet or cable.

The server may be in the control of an insurance company or may beaccessible by a number of insurance companies or automated settlementengine operators. The sever may function as an image analysis system120. In some embodiments, the system comprises an image analysis system120. The image analysis system may comprise an image receiver, a featureextractor, and/or a feature analyzer. The image analysis system may be afunction performed by one or more processors on a server or apersonal-computing device. The image analysis system may accept, asinput, one or more image files. The one or more image files accepted bythe image analysis system may be in the form of JPEG, IMG, PNG, bitmap,or other image file. The image analysis system may also accept as inputone or more video files. The one or more video files may be in the formof MOV, MPEG, AVI, or other format of video file. The image and/or videofiles may also carry data in the form of metadata or tags. Additionallyor alternatively, the image and/or video files may be arranged insubsets of folders providing additional information. The additionalinformation or metadata may include information such as address or GPSor some other location data associated with the collected image or videofile, a timestamp, an operator identifier, insurance companyinformation, and/or other information. After receiving the files, theimage analysis system may use a feature extractor function toautomatically determine information regarding the property system 112associated with the captured and received images and/or video files.

In some embodiments, the image analysis system 120 and automatedsettlement engine 116 may be implemented in a common entity and mayreside in a common enterprise network (e.g., behind a common firewall).In other embodiments, the systems 116, 120 may be implemented bydifferent entities and/or at different enterprise networks. In someembodiments, the automated settlement engine 116 and image analysissystem 120 may be implemented on a common server and that particularserver may have some or all of the components of both the automatedsettlement engine 116 and image analysis system 120. The depiction ofthe image analysis system 120 as being separate from the automatedsettlement engine 116 is for illustrative purposes only and should notbe construed as limiting the communication system to any particularconfiguration.

In accordance with at least some embodiments of the present disclosure,the communication network 104 may comprise any type of knowncommunication medium or collection of communication media and may useany type of protocols to transport messages between endpoints. Thecommunication network 104 may include wired and/or wirelesscommunication technologies. The Internet is an example of thecommunication network 104 that constitutes an Internet Protocol (IP)network consisting of many computers, computing networks, and othercommunication devices located all over the world, which are connectedthrough many telephone systems and other means. Other examples of thecommunication network 104 include, without limitation, a standard PlainOld Telephone System (POTS), an Integrated Services Digital Network(ISDN), the Public Switched Telephone Network (PSTN), a Local AreaNetwork (LAN), a Wide Area Network (WAN), a Voice over Internet Protocol(VoIP) network, a Session Initiation Protocol (SIP) network, a cellularnetwork, and any other type of packet-switched or circuit-switchednetwork known in the art. In addition, it can be appreciated that thecommunication network 104 need not be limited to any one network type,and instead may be comprised of a number of different networks and/ornetwork types. The communication network 104 may comprise a number ofdifferent communication media such as coaxial cable, copper cable/wire,fiber-optic cable, antennas for transmitting/receiving wirelessmessages, and combinations thereof.

The image capture device(s) 108 may be configured to capture one or moreimages (still or motion) of a property system 112 and provide the imagesto the image analysis system 120 via the communication network 104 orsome other image-delivery mechanism. As shown in FIG. 2, the imagesreceived at the image analysis system 120 correspond to original roofimage(s) 204. The image analysis system 120 may employ the imagereceiver 124 to initially receive and store the original roof image(s)204 from the image capture device(s) 108.

The image analysis system 120 may then utilize automated imageprocessing technologies (e.g., contrast detection, contrast adjustment,color-to-greyscale conversion, grayscale-to-black-and-white conversion,pixel color or brightness analysis, adjacent pixel analysis, etc.) toautomatically convert the original roof image(s) 204 intoenhanced/processed roof image(s) 208. These enhanced/processed roofimage(s) 208 may then be provided to the feature extractor 128, whichanalyzes the image(s) to identify locations of features, highlightfeatures, and/or mark features found within the images. It should beappreciated that the feature extractor 128 may operate on original roofimage(s) 204 in addition to or in lieu of operating onenhanced/processed roof image(s) 208.

The image analysis system 120 may then employ a feature analyzer 132 toautomatically analyze the extracted features 212 to develop a featurelist 216. In some embodiments, the feature analyzer 132 may develop afeature list 216 that may include a textual description of features andtheir locations (relative to one another and/or relative to a fixedpoint on the property system 112). The feature list 216 may furtherinclude a description of the type, size, and shape of each feature inthe extracted features 212. These features may correspond to locationswhere potential damage is visible on the property system 112. As anexample, extracted features 212 may correspond to identified locationsin the original roof image that exhibit a visual similarity to damageshingles, gutters, vents, windows, siding, bricks, or other materials.This damage may be visible and identifiable via the image processing andfeature extraction processes described herein. The feature analyzer 132may format the feature list 216 in such a way that the feature list 216is deliverable to an automated settlement engine 116.

FIG. 3 shows a method in accordance with one embodiment that may beemployed to generate the feature list 216. In particular, raw roofimages may be received (step 304) and then have image processing appliedthereto by the image analysis system 120 (step 308). Thereafter, thefeatures found within the image(s) may be automatically marked and theirlocations identified (whether relative to other features or a specificobject included in the image, e.g., a coin, dollar bill, roof corner,roof peak, etc.) (step 312). The feature analyzer 132 may thenautomatically generate a description of the features (e.g., shape, size,etc.) along with the feature locations and identification of otherobjects (e.g., non-roof objects) (step 316). This description can thenbe delivered (presumably as a feature list 216) to the automatedsettlement engine 116 for further processing as described herein (step320).

In some embodiments, the automated settlement engine 116 includes animage analysis system API 136 that provides an interface for the imageanalysis system 120. The API 136 may define rules for delivering thefeature list 216 (e.g., data structure, file format, maximum charactersper entry, location definition requirements, etc.). The image analysissystem API 136 may provide the conduit through which the feature list216 is delivered to the automated settlement engine 116.

As shown in FIG. 4, once the feature list 216 is received at theautomated settlement engine 116 (step 404), the automate settlementengine 116 may begin employing its other components to analyze theoverall damage to the roof to determine, in an automated orsemi-automated fashion, whether the damage corresponds to hail or someother type of damage that occurred naturally and it, therefore, subjectto insurance coverage and reimbursement.

The automated settlement engine 116 may include a set of insurancecarrier-specific guidelines 140 that are used to define how theautomated settlement engine 116 operates on a specific set or subset ofimages. More specifically, if the images are from a property system 112insured by insurance carrier A, then a first set of rules may be appliedto the analysis of the images where if the images are from a propertysystem 112 insured by insurance carrier B, then a second set of rulesmay be applied to the analysis of the images (assuming there is adifference between insurance carrier A and carrier B's guidelines 140).

The automated settlement engine 116 may also include image analysisrules 144 and rules for analyzing non-image information 148. Finally,the automated settlement engine 116 may include a weighting engine 152and reporting engine 156.

Thus, to complete the processing depicted in FIG. 4, the methodcontinues with the automated settlement engine 116 receiving informationregarding non-image objects (step 408). This type of information caninclude a description of non-roof objects (e.g., gutters, siding,fencing, trees, etc.) that help describe the environment surrounding theproperty system 112. The roof images may then be analyzed using theimage analysis rules 144. In particular, the image analysis rules 144may analyze the locations of features from the images to see if there isa random, non-random, or ordered characteristic to their distribution.The image analysis rules 144 may also analyze whether or not theentirety of the property system 112 was subjected to damage (e.g., hasfeatures located thereon) or whether only a subset of the propertysystem 112 has been subjected to damage. As an example, only North orWest facing roof portions may have been subjected to damage whereas theSouth and East facing roof portions were not. Hail damage is alsousually associated with random or non-ordered locations of damage withina damage area. If the image analysis rules 144 identify an order orpattern to the damage or locations of features, then it may bedetermined that actual hail damage did not occur and is, therefore, notsubject to insurance coverage. In some embodiments, an elevationinspection may be automatically performed prior to a roof inspection. Onthe elevation inspection, analysis may include an inspection for damageand non-damage to items that are commonly damaged by hail. These items,in some embodiments, may correspond to items constructed of soft metals,plastics, and fiberglass. Examples of such items/objects include,without limitation, gutters, downspouts, window screens, dryer vents, ACfins, etc. It is rare to find damage to the roof shingles withoutfinding supporting evidence on the elevations. On the roof, soft metalsand plastic vents should also show damage. Lack of such damage mayprovide an indication of non-hail damage to the property system 112.

The image analysis rules 144 applied by the automated settlement engine116 may depend upon the carrier-specific guidelines 140 being employed.The application of non-image analysis rules 148 may also depend upon thecarrier-specific guidelines 140. Analysis of the different aspects ofimages and non-image information may then be weighted according to aweighting engine 152. In some embodiments, a weighting is determinedbased on the carrier-specific guidelines 140 (step 412) and thedetermined weighting is applied by the weighting engine 152 (step 416).The different factors that can be weighted differently depending uponcarrier-specific guidelines 140 include, without limitation, number offeatures/hail strikes identified within a predetermined area, size offeatures/hail strikes, shapes of features/hail strikes, relativelocations of features/hail strikes, total extent of overall hail damageto roof, environmental/non-roof damage (e.g., tree damage, sidingdamage, gutter damage, etc.), age of roof, roofing material, age ofhouse, reported damage of surrounding houses, etc. The applied weightingcan be used to develop a probability score as to whether or not the roofactually experienced hail damage in a particular location or along itsentirety (step 420). Embodiments of the present disclosure could alsoweigh these factors (e.g., video/still image information along withadditional information not obtained from video and/or still images).This information can then be included in a report generated by areporting engine 156 that is delivered to the insured, the insurancecarrier, and/or an operator of the automated settlement engine 116and/or image analysis system 120.

As illustrated in FIG. 5, a method of automatically identifying damagemay be achieved with the systems described herein. Such a method maybegin in step 504 with an AI system receiving data from a propertysystem. As described herein, data may be received from a property systemfrom a number of sources. For example, sensor data may be transmittedvia a communication network 104, data may be input into a user device160, photographs and/or videos may be taken by a camera or drone 108, orother sources.

Using any received data, an AI system may determine whether an eventoccurred in step 508. An event may be the occurrence of damage to anyproperty within a property system 112, or may be a natural disaster suchas an earthquake, high winds, heavy rain, hail, etc. For example,moisture sensors in an attic may indicate a leak has occurred, pressuresensors on a roof may indicate hail has occurred, etc.

If the AI system determines no event and/or damage has occurred in step508, the method may comprise retuning to step 504 and continuing toreceive and/or wait for data related to the property system. If, on theother hand, the AI system determines an event and/or damage has occurredin step 508, the method may comprise proceeding to step 512 in whichfeatures of the property system associated with the event and/or damagemay be identified by the AI system.

In some embodiments, the AI system may additionally prompt a user toprovide additional information. For example, the AI system may determinea possibility of damage and may send a request to a user device for auser to input data related to the possibility of damage. The request maybe in the form of a series of prompts and/or questions to guide a userthrough a process of collecting data.

In step 516, the AI system may compile the features identified in step512 and may generate a list of features associated with the eventdetected in step 508.

In some embodiments, the AI system may compare the generated featurelist with one or more previous feature lists to identify duplicateobjects listed in the present feature list. Such a step may enable theAI system to verify no damage will be covered twice by an insurancecarrier.

In step 520, the AI system may determine a type of damage for eachfeature in the list of features generated in step 516. For example, thelist may be updated to include an indication of damage and/or anindication of a possibility of damage for each features. Indications ofpossibility of damage may further include a rating related to a degreeof certainty for the possibility of damage.

In some embodiments, the AI system may determine a cause and/or originof the damage for each of the features. For example, the AI system mayreview all of the damage together and identify a likely cause of thedamage in light of the damage to all features. For example, if all ofthe damage is to features within a single room the AI system maydetermine the damage is contained within the room. Similarly, if all thedamage is in the basement and the damage is all related to water damage,the AI system may determine the cause of the damage is likely a leak inthe basement.

In some embodiments, the AI system may determine one or more featuresnot listed in the list which may be damaged based on the damage listedin the list. For example, the AI system may be capable of identifyingfeatures associated with other features and which are often damaged whenother features are damaged. Any features not currently listed but whichmay be damaged may be identified by the AI system and may be used toinstruct users to provide additional data related to the damage.

In step 524, the AI system may compare the feature list and the type ofdamage as determined by the AI system with one or more insurancepolicies related to the property system. For example, the AI system maydetermine which feature in the feature list is covered by insurance andmay determine whether the type of damage to each feature is of the typecovered by an active insurance policy.

In some embodiments, the AI system may prompt a user with questionsrelated to additional information needed by an insurance carrier. Forexample, a user may be prompted with a series of questions targeted todetermining whether an insurance claim was fraudulently made. Forexample, the user posed with questions may be an onsite adjuster and/orinspector and the questions may be related to claims and/or statementsmade by the property owner. The questions may assist the adjuster and/orinspector to verify such claims and/or statements.

As extensions to the above-described solutions, embodiments of thepresent disclosure contemplate the following. When there is damage tothe roof that the system determines is not hail, it may be possible toprovide a probability of what the non-hail damage corresponds to. Forexample, organic shingles are manufactured with paper instead offiberglass, so over time they are prone to blister popping (spots ofgranule loss) due to getting wet and the moisture evaporating andpopping granules off. Additionally, the south slope wears out thefastest due to the extra sun it receives. In some embodiments, haildamage probability could be expanded to all types of roof damage (wind,blister popping, nail pops, mechanical damage, etc.) It should also beappreciated that embodiments of the present disclosure also includesolutions where an inspector manually inspects the roof and takes photosfor review, as opposed to an automated device such as a drone.

As can be seen from the above description, the systems and methodsdisclosed herein are useful for automating the process of determiningwhether or not hail damage has been inflicted upon a roof. Specificdetails were given in the description to provide a thoroughunderstanding of the embodiments. However, it will be understood by oneof ordinary skill in the art that the embodiments may be practicedwithout these specific details. For example, well-known circuits,processes, algorithms, structures, and techniques have been shownwithout unnecessary detail in order to avoid obscuring the embodiments.Persons of ordinary skill in the art will also understand that variousembodiments described above may be used in combination with each otherwithout departing from the scope of the present disclosure.

While illustrative embodiments of the disclosure have been described indetail herein, it is to be understood that the inventive concepts may beotherwise variously embodied and employed, and that the appended claimsare intended to be construed to include such variations, except aslimited by the prior art.

Moreover, aspects of the present disclosure may take the form of anentirely hardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Any combinationof one or more computer readable medium(s) may be utilized. The computerreadable medium may be a computer readable signal medium or a computerreadable storage medium.

A computer readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Specific details were given in the description to provide a thoroughunderstanding of the embodiments. However, it will be understood by oneof ordinary skill in the art that the embodiments may be practicedwithout these specific details. For example, circuits may be shown inblock diagrams in order not to obscure the embodiments in unnecessarydetail. In other instances, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the description.

What is claimed is:
 1. A system, comprising: a processor; and a memorycoupled to the processor and comprising computer-readable program codethat when executed by the processor causes the processor to performoperations comprising: receiving data associated with a property; basedon the received data, determining an event has occurred; automaticallyidentifying features and corresponding locations of the identifiedfeatures from the data; generating a feature list describing theidentified features and the corresponding locations of the identifiedfeatures; after generating the feature list has occurred, identifyingone or more insurance policies associated with the property; analyzingthe identified features from the feature list along with thecorresponding locations of the identified features to determine a typeof damage associated with the property; comparing the damage associatedwith the property to the one or more insurance policies associated withthe property; and presenting one or more questions to a user, whereinthe questions are directed to identifying a possibility of fraud.
 2. Thesystem of claim 1, wherein the damage associated with the propertyoccurred inside the property.
 3. The system of claim 2, wherein the datarelates to a flood line.
 4. The system of claim 3, wherein theoperations further comprise identifying one or more items within theproperty which may be damaged based on the flood line.
 5. The system ofclaim 1, wherein the operations further comprise prompting a user toinput additional information.
 6. The system of claim 1, wherein theoperations further comprise comparing the feature list to a pre-existingfeature list to identify items damaged prior to the event.
 7. The systemof claim 1, wherein the operations further comprise determining a causeof origin of the damage.
 8. A method for surveying damage to a property,the method comprising: performing operations as follows on a processorof a computing device: receiving data associated with the property;based on the received data, determining an event has occurred;automatically identifying features and corresponding locations of theidentified features from the data; generating a feature list describingthe identified features and the corresponding locations of theidentified features; after generating the feature list has occurred,identifying one or more insurance policies associated with the property;analyzing the identified features from the feature list along with thecorresponding locations of the identified features to determine a typeof damage associated with the property; comparing the damage associatedwith the property to the one or more insurance policies associated withthe property; and prompting a user to input additional information. 9.The method of claim 8, wherein the damage associated with the propertyoccurred inside the property.
 10. The method of claim 9, wherein thedata relates to a flood line.
 11. The method of claim 10, furthercomprising identifying one or more items within the property which maybe damaged based on the flood line.
 12. The method of claim 8, furthercomprising comparing the feature list to a pre-existing feature list toidentify items damaged prior to the event.
 13. The method of claim 8,further comprising determining a cause of origin of the damage.
 14. Themethod of claim 8, further comprising presenting one or more questionsto a user, wherein the questions are directed to identifying apossibility of fraud.
 15. A computer program product, comprising: anon-transitory computer readable storage medium having computer readableprogram code embodied therewith, the computer readable program codeconfigured when executed by a processor to: receive data associated witha property; based on the received data, determine an event has occurred;automatically identify features and corresponding locations of theidentified features from the data; generate a feature list describingthe identified features and the corresponding locations of theidentified features; after generating the feature list, identify one ormore insurance policies associated with the property; analyze theidentified features from the feature list along with the correspondinglocations of the identified features to determine a type of damageassociated with the property; and compare the damage associated with theproperty to the one or more insurance policies associated with theproperty; and compare the feature list to a pre-existing feature list toidentify items damaged prior to the event.
 16. The computer programproduct of claim 15, wherein the damage associated with the propertyoccurred inside the property.
 17. The computer program product of claim16, wherein the data relates to a flood line.
 18. The computer programproduct of claim 17, wherein computer readable program code is furtherconfigured to identify one or more items within the property which maybe damaged based on the flood line.
 19. The computer program product ofclaim 15, wherein the computer readable program code is furtherconfigured to prompt a user to input additional information.
 20. Thecomputer program product of claim 15, wherein the computer readableprogram code is further configured to determine a cause of origin of thedamage.